This paper presents a new algorithm, called steady-state and generational evolutionary
algorithm, which combines the fast and steadily tracking ability of steady-state algorithms …

Most of the real-world optimization problems have multiple objectives to deal with. Satisfying
one objective at a time may lead to the huge deviation in other. Therefore, an efficient tool is …

Multi-objective evolutionary algorithm (MOEA) has become a common and effective method
to solve real-world multi-objective optimization problems. However, in some practical …

This paper establishes a multi-level progressive optimization model of planetary roller screw
mechanism (PRSM) to achieve a more uniform load distribution, lower contact stress …

Abstract Though the Genetic Algorithm (GA) has received considerable attention recently in
solving multi-objective optimization problems, inefficiency regarding performance has been …

This paper proposes the multi-objective optimization of a double-stator hybrid-excited flux-
switching permanent-magnet (DSHE-FSPM) machine, which aims at the contradictory …

In multi-objective optimization problems (MOPs), several mutually conflicting objectives are
optimized simultaneously. In such scenarios, there is not a unique solution to the problem; …

Nowadays, one critical challenge of cybersecurity administrators is the protection of online
resources from network intrusions. Despite several academic and industry research …

Computational effectiveness and limited resources in evolutionary algorithms are
interdependently handled during the working of low-power microprocessors for real-world …

Most multiobjective optimization algorithms maintain an archive explicitly or implicitly during
their search. Such an archive can be solely used to store high-quality solutions presented to …